In recent years, with the development of bearer technologies, the coding quality of audio coders becomes more and more satisfying to people's requirements. Currently, most of the standardized audio coders encode signals by combining the transform coding technology, the psychoacoustic model, and the lattice vector quantization technology, for example, the Motion Picture Experts Group (MPEG) layer 3, Advanced Audio Coding (AAC) standard, and DolbyAC-3 standard. The transform coding technology and the lattice vector quantization technology are widely applicable in the broadband and Super Wide Band (SWB) voice field and the audio signal coding field. Lattice vector quantization is a type of algebraic vector quantization. Lattice vector quantization makes up a regular lattice in a multi-dimensional signal space. The points in the lattice are called “lattice points”. The signal space is divided into cells by using the lattice points as quantization vectors.
A method for generating a lattice vector quantizer codebook is put forward in the prior art. For example, the method can be implemented through an N-dimensional (N≧2) multi-rate Gosset lattice vector quantizer. The quantizer includes M (M≧2) lattice vector quantizers, each of which has a different Number of Code Bits (NCB). If the codebook corresponding to the lattice vector quantizer of the NCB numbered i (NCBi) is expressed as QRiN, iε[0,M−1], the codebook of the whole N-dimensional multi-rate lattice vector quantizer is Q{QR0N, QR1N, . . . , QRM-1N}. The codebook of a Gosset lattice vector quantizer may be generated out of some basic root leader vectors through transformation of the signs and positions of the elements. Therefore, under each lattice vector quantizer of a specified NCB in the N-dimensional multi-rate Gosset lattice vector quantizer, the codebook is generated through the stored root leader vector. A more detailed implementation process is: First, the amplitude vector and the length vector corresponding to the root leader vector under the lattice vector quantizer of each specified NCB are stored; the amplitude vector represents the values of different nonzero elements in the corresponding vector, where the values of the elements are arranged in descending order; the length vector represents the count of occurrences of each nonzero element value in the corresponding vector. The root leader vector is calculated according to the amplitude vector and length vector corresponding to the root leader vector, and the codebook of the lattice vector quantizer of each specified NCB is generated according to transformation of the signs and positions of elements of the root leader vector.
Supposing that the codebook corresponding to the lattice vector quantizer of NCBi is expressed as QRiN, this codebook may be generated through Li root leader vectors. The root leader vector numbered k may be calculated according to the corresponding amplitude vector μ(i,k)=└μ0(i,k) μ1(i,k) . . . μLP(i,k)-1(i,k)┘ and length vector W(i,k)=└w0(i,k) w1(i,k) . . . wLP(i,k)-1(i,k)┘, where kε[0,Li−1], and LP(i,k) is the number of dimensions of the amplitude vector and length vector of the root leader vector numbered k corresponding to the lattice vector quantizer of NCBi, and represents the number of elements with different values in the root leader vector. Therefore, the codebook of the whole N-dimensional multi-rate Gosset lattice vector quantizer may be represented by M two-dimensional amplitude vector tables and M two-dimensional length vector tables. The amplitude vector set corresponding to lattice vector quantization of the lattice vector quantizer of NCB0 is {μ(0,0), μ(0,1), . . . , μ(0,k), . . . , μ(0,L0-1)}; the amplitude vector set corresponding to lattice vector quantization of the lattice vector quantizer of NCB1 is {μ(1,0), μ(1,1), . . . , μ(1,k), . . . , μ(1,L1-1)}; the amplitude vector set corresponding to lattice vector quantization of the lattice vector quantizer of NCBi is {μ(i,0), μ(i,1), . . . , μ(i,k), . . . , μ(i,Li-1)}; and the amplitude vector set corresponding to lattice vector quantization of the lattice vector quantizer of NCBM-1 is {μ(M-1,0), μ(M-1,1), . . . , μ(M-1,k), . . . , μ(M-1,LM-1-1)}, where μ(i,k)=└μ0(i,k) μ1(i,k) . . . μLP(i,k)-1(i,k)┘. The length vector set corresponding to lattice vector quantization of the lattice vector quantizer of NCB0 is {W(0,0), W(0,1), . . . , W(0,k), . . . , W(0,L0-1)}; the length vector set corresponding to lattice vector quantization of the lattice vector quantizer of NCB1 is {W(1,0), W(1,1), . . . , W(1,k), . . . , W(1,L1-1)}; the length vector set corresponding to lattice vector quantization of the lattice vector quantizer of NCBi is {W(i,0), W(i,1), . . . , W(i,k), . . . , W(i,Li-1)}; and the length vector set corresponding to lattice vector quantization of the lattice vector quantizer of NCBM-1 is {W(M-1,0), W(M-1,1), . . . , W(M-1,k), . . . , W(M-1,LM-1-1)}, where W(i,k)=└w0(i,k) w1(i,k) . . . wLP(i,k)-1(i,k)┘. Therefore, the total storage capacity is
  2  ⁢            ∑              i        =        0                    M        -        1              ⁢                  ∑                  k          =          0                                      L            i                    -          1                    ⁢                        L          P                      (                          i              ,              k                        )                          .            
In the foregoing process, when a quantizer includes many lattice vector quantizers of different NCBs and each NCB corresponds to many root leader vectors, the storage overhead of the amplitude vectors and length vectors is very heavy, which leads to a waste of the storage space.